function y = FIGs_C1_C2_C3_C4_irf_dynare(dr, e1, long, drop, replic, iorder)
% This is a Dynare file.
% function y = irf(dr, e1, long, drop, replic, iorder)
% Computes impulse response functions
% 
% INPUTS
%    dr:     structure of decisions rules for stochastic simulations
%    e1:     exogenous variables value in time 1 after one shock
%    long:   number of periods of simulation
%    drop:   truncation (in order 2)
%    replic: number of replications (in order 2)
%    iorder: first or second order approximation
%
% OUTPUTS
%    y:      impulse response matrix
%        
% SPECIAL REQUIREMENTS
%    none

% Copyright (C) 2003-2010 Dynare Team
%
% This file is part of Dynare.
%
% Dynare is free software: you can redistribute it and/or modify
% it under the terms of the GNU General Public License as published by
% the Free Software Foundation, either version 3 of the License, or
% (at your option) any later version.
%
% Dynare is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
% GNU General Public License for more details.
%
% You should have received a copy of the GNU General Public License
% along with Dynare.  If not, see <http://www.gnu.org/licenses/>.

global M_ oo_ options_ y0

%rng(72)
randn('seed',72)


if M_.maximum_lag >= 1
    temps = repmat(dr.ys,1,M_.maximum_lag);
else
    temps = zeros(M_.endo_nbr, 1); % Dummy values for purely forward models
end
y       = 0;

temps = y0;

if iorder == 1
    i_exo_var = setdiff([1:M_.exo_nbr],find(diag(M_.Sigma_e) == 0 ));
    nxs = length(i_exo_var);
    ex1 = zeros(long+drop,M_.exo_nbr);
    ex2 = ex1;
    chol_S = chol(M_.Sigma_e(i_exo_var,i_exo_var));
    for j = 1: replic
        ex1(:,i_exo_var) = randn(long+drop,nxs)*chol_S;
        ex2 = ex1;
        ex2(drop+1,:) = ex2(drop+1,:)+e1';   
        y1 = FIGs_C1_C2_C3_C4_simult_r(temps,dr,ex1,iorder);
        y2 = FIGs_C1_C2_C3_C4_simult_r(temps,dr,ex2,iorder);
        y = y+(y2(:,M_.maximum_lag+drop+1:end)-y1(:,M_.maximum_lag+drop+1:end));
    end
    y=y/replic;
else
    % eliminate shocks with 0 variance
    i_exo_var = setdiff([1:M_.exo_nbr],find(diag(M_.Sigma_e) == 0 ));
    nxs = length(i_exo_var);
    ex1 = zeros(long+drop,M_.exo_nbr);
    ex2 = ex1;
    chol_S = chol(M_.Sigma_e(i_exo_var,i_exo_var));
    for j = 1: replic
        ex1(:,i_exo_var) = randn(long+drop,nxs)*chol_S;
        ex2 = ex1;
        ex2(drop+1,:) = ex2(drop+1,:)+e1';   
        y1 = FIGs_C1_C2_C3_C4_simult_r(temps,dr,ex1,iorder);
        y2 = FIGs_C1_C2_C3_C4_simult_r(temps,dr,ex2,iorder);
        y = y+(y2(:,M_.maximum_lag+drop+1:end)-y1(:,M_.maximum_lag+drop+1:end));
    end
    y=y/replic;
end
